Journal of Scientific Research and Reports

Unstable soil-related disasters are a concern for modern urbanization and development. Hence, it is necessary to stabilize the soil and estimate the geotechnical properties using advanced methodologies, one of the best-suited methods for safe ground to construct any infrastructure.  Accordingly, this study has provided machine learning techniques, namely Multiple Linear Regression (MLR), Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF), to estimate the CBR_s of soil. The dataset used in the study comprised 15 observations. The data consists of input variables including the percentages of fly ash (%FA), cement (%C), and coir fiber (%CF), along with optimum moisture content (OMC), maximum dry density (MDD), and soaked California Bearing Ratio (CBR). The dataset is divided into a training set (55% of the total data) and a testing set (45%). The performance of the developed models was achieved using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r), and volumetric efficiency (VE). For the training set, MLR achieved RMSE = 1.34, MAE = 1.08, r = 1.00, and VE = 0.96, while MARS recorded 7.06, 6.03, 0.92, and 0.77, respectively. In the testing set, MLR again outperformed with 0.86, 0.80, 1.00, and 0.98, compared to MARS, which obtained 3.22, 2.59, 0.99, and 0.92. This study introduces a novel approach by evaluating the predictive capabilities of linear and non-linear machine learning algorithms for soaked CBR estimation using a limited geotechnical dataset, thereby providing insights into model robustness and reliability in data-constrained scenarios.